Great Games
2022-10-04
1 Overview
Our research question is: how to audit whether labels are “conceptualized consistently” across different countries and different languages. We propose a framework to audit labels themselves for world-wide inclusivity. Here, we are not concerned with a specific ML implementation, and instead audit the labels that are being fed into systems.
Figure 1 is descriptive statistics of our survey.
- We consider country comparisons in three ways:
- Within each country, how much “internal” agreement is there on labels of interest? (Figure 2a)
- How do different populations view these labels in specific contexts? (Figure2b)
- If label outcome differences are found across countries, is there a data-backed explanation relating to culture? (Figure3)
- We consider language comparison in three ways:
- How similar are label choices for bilingual speakers when asked questions in their native language versus English? (Figure4)
- How similar are label choices for English-speakers across English-speaking countries? (Figure5)
- How similar are label choices for speakers of non-English language? (Figure6)
Figure 5 and 6 should look similar to Figure 2b using similar approach and have not been plotted currently in this doc.
bookdown::serve_book()2 Figure 1: Discriptive Statistics
source("read.R")
head(df)## # A tibble: 6 × 17
## Response.ID game country GENDER AGE ageCut native Langu…¹ region exper…²
## <chr> <chr> <fct> <chr> <int> <fct> <fct> <chr> <fct> <dbl>
## 1 US_English_No… PUBG US Male 45 45-55 Engli… English US 5
## 2 US_English_No… PUBG US Male 45 45-55 Engli… English US 5
## 3 US_English_No… PUBG US Male 45 45-55 Engli… English US 5
## 4 US_English_No… PUBG US Male 45 45-55 Engli… English US 5
## 5 US_English_No… PUBG US Male 45 45-55 Engli… English US 5
## 6 US_English_No… PUBG US Male 45 45-55 Engli… English US 5
## # … with 7 more variables: hardcore <fct>, accessibility <fct>, labeler <chr>,
## # ambassador <chr>, label <fct>, answer <int>, gender <chr>, and abbreviated
## # variable names ¹Language, ²experience
## # ℹ Use `colnames()` to see all variable names2.1 Participant breakdown by gender, ageCut, hardcore, accessibility, country, game
# 5,991 participants in total
nrow(df %>% distinct(Response.ID))## [1] 5991# gender, age, experience, accessibility (disability) breakdowns
# gender: Male, Female, and Nonbinary.
df %>% distinct(gender, Response.ID) %>% count(gender)## # A tibble: 3 × 2
## gender n
## <chr> <int>
## 1 Female 432
## 2 Male 5497
## 3 NonBinary 62# ageCut: 18-24, 25-34, 35-44, 45-55, 55+. Per Xbox internal age breakdowns.
df %>% distinct(ageCut, Response.ID) %>% count(ageCut)## # A tibble: 6 × 2
## ageCut n
## <fct> <int>
## 1 18-24 1182
## 2 25-34 1620
## 3 35-44 1641
## 4 45-55 773
## 5 55+ 111
## 6 <NA> 664# hardcore: hardcore (play games over 1 time a week), non-hardcore (play games 1 time or less)
df %>% distinct(hardcore, Response.ID) %>% count(hardcore)## # A tibble: 2 × 2
## hardcore n
## <fct> <int>
## 1 hardcore 5608
## 2 non-hardcore 383# accessibility: or really disability, Yes/No. Those people who incated that they have disability or not
df %>% distinct(accessibility, Response.ID) %>% count(accessibility)## # A tibble: 4 × 2
## accessibility n
## <fct> <int>
## 1 No 5466
## 2 Prefer not to answer 112
## 3 Yes 328
## 4 <NA> 85# countries
df %>% distinct(country, Response.ID) %>% count(country)## # A tibble: 16 × 2
## country n
## <fct> <int>
## 1 US 3040
## 2 Germany 245
## 3 Poland 123
## 4 Greece 319
## 5 Japan 237
## 6 Korea 165
## 7 Singapore 69
## 8 India 99
## 9 Saudi.Arabia 35
## 10 South.Africa 212
## 11 Nigeria 170
## 12 Brazil 280
## 13 Argentina 182
## 14 Colombia 165
## 15 Chile 193
## 16 Mexico 457# games
df %>% distinct(game, Response.ID) %>% count(game)## # A tibble: 11 × 2
## game n
## <chr> <int>
## 1 Animal Crossing: New Horizons 1102
## 2 Call of Duty: Vanguard 1718
## 3 Candy Crush 799
## 4 Elden Ring 1896
## 5 FIFA22 1123
## 6 Grand Theft Auto V 3168
## 7 Mario Kart 8 1651
## 8 Minecraft 2631
## 9 PUBG 1241
## 10 Stardew Valley 1043
## 11 The Sims 3 4292.2 Label correlations
- In this plot, we look at correlation between labels from our sample. We can observe that difficulty, violent, action, action.motivation, control_complexity, strategy, learning_curve tend to go together. Comedic, creativity, pacifist, zen,made.for.kids, cozy are clustered together.
library(corrplot)## corrplot 0.92 loaded# disregard the NA
res <- df %>%
filter(!(label %in% c("NA.positive.opinion", "NA.negative.opinion", "NA.feeling", "NA.art"))) %>%
mutate(label=factor(label, levels=unique(df$label))) %>% # keep order in the final graph Figure 1
select(Response.ID, label, answer) %>%
pivot_wider(., names_from = label, values_from = answer) %>%
unnest() %>%
select(-Response.ID)
mx <- cor(res, use = "complete.obs")
corrplot(mx, type = "upper", order = "FPC", tl.cex=2,
tl.col = "black", tl.srt = 45, title="Figure 1: Label Correlation")
3 Figure 2a: Consider country comparisons in three ways
This step is optional. We remove games where a country has very few response (< 5). In other words, we find games with >=5 participants across all countries.
# filter out countries with few data point
# for each game, remove countries with few data points
constraints <- df %>% count(game, country, label) %>% # group by country and label
select(game, country, n) %>% distinct() %>%
filter(n < 5)
# remove games that have too few response in any country
df <- df %>% filter(!(game %in% unique(constraints$game))) %>%
filter(!(label %in% c("NA.positive.opinion", "NA.negative.opinion", "NA.feeling", "NA.art")))
# game included
unique(df$game)## [1] "PUBG" "Grand Theft Auto V" "Minecraft"
## [4] "Elden Ring" "FIFA22" "Call of Duty: Vanguard"3.1 Within each country, how much “internal” agreement is there on labels of interest?
We seek to answer questions such as does the concept of a “cozy game” elicit more agreement within the US than it does in Japan? To achieve so, we calculate the average standard deviation/variance of label outcomes and comparing across countries. We plot standard deviation to reduce the length of this document.
Code Description: plotFigure2a is a wrapper function that plots all 28 label output. The first three are 1-3 scale, 1-4 scale for difficulty the latter 24 are 0-1 scale. plotVariance is the plotting function.
# wrapper function that enable individual game
plotFigure2a <- function(game = "All", sd=FALSE, debug=FALSE) {
var.df <- df %>% group_by(game, country, label) %>%
summarise(mean_answer = mean(answer, na.rm=TRUE),
variance = var(answer, na.rm=TRUE),
sd=sd(answer, na.rm=TRUE))
if(game == "All" && !sd) {
# average the variances across games for each country
country.df <- var.df %>% group_by(country, label) %>%
summarise(y=mean(variance, na.rm=TRUE))
} else if (game == "All" && sd) {
country.df <- var.df %>% group_by(country, label) %>%
summarise(y=mean(sd, na.rm=TRUE))
} else if(game != "All" && !sd) {
country.df <- var.df %>% filter(game == {{ game }}) %>% mutate(y=variance)
} else if (game != "All" && sd) {
country.df <- var.df %>% filter(game == {{ game }}) %>% mutate(y=sd)
} else {
print("Error...")
}
p1 <- plotVariance(country.df, gameTitle=game, sd=sd, binary=FALSE)
p2 <- plotVariance(country.df, gameTitle=game, sd=sd, binary=TRUE)
res <- ggarrange(p1, p2, ncol=1, heights = c(1, 6))
return(res)
}
# plot variance of a label across countries
plotVariance <- function(inputDf, gameTitle="All", sd=FALSE, binary=FALSE) {
# color the US
inputDf$color <- inputDf$country == "US"
plotDf <- inputDf[with(inputDf, order(label, country, y)),] %>%
filter(!label %in% c("control_complexity", "learning_curve", "difficulty", "replayability"))
if(!binary) {
plotDf <- inputDf[with(inputDf, order(label, country, y)),] %>%
filter(label %in% c("control_complexity", "learning_curve", "difficulty", "replayability"))
}
if(sd) {
yLabel <- "Standard Devaition"
} else {
yLabel <- "Variance"
}
p <- plotDf %>% ggplot(aes(x=reorder_within(country, y, label), y=y, color=color)) +
geom_point() +
scale_x_reordered() +
facet_wrap(~label, ncol=4, scales = "free") +
ylim(c(0, 3)) +
scale_color_manual(values=c("#999999", "#56B4E9")) +
labs(y = yLabel, x = NULL,
title = paste0("How much internal agreement is there on labels of interest (", gameTitle, ")"))
if(binary) {
p <- p + ylim(c(0, 1))
}
return(p)
}3.1.1 Taken all games together.
Here we look at the standard deviation across all the games. We look at all the games together. (e.g., Does the concept of a “cozy game” elicit more agreement within the US than it does in Japan regardless of games?) The US is highlighted in the graph in Figure 2a.
We can observe that Saudi Arabia with the highest variability (i.e., standard deviation) 11 times, South Africa 2 times, Nigeria 7 times, Singapore 4 times, Japan 1 time, India 1 time, Mexico 1 time, and Colombia 1 time.
plotFigure2a("All", sd=TRUE, debug=FALSE)
3.1.2 Breakdown by game
Here we look at the variance/standard deviation in individual games. We look at all the games together. (e.g., Does the concept of a “cozy game” elicit more agreement within the US than it does in Japan in specific games?)
Note that not in one time did the US has the highest standard deviation.
3.1.2.1 PUBG
plotFigure2a("PUBG", sd=TRUE, debug=FALSE)## `summarise()` has grouped output by 'game', 'country'. You can override using
## the `.groups` argument.
## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.
3.1.2.2 Grand Theft Auto V
plotFigure2a("Grand Theft Auto V", sd=TRUE, debug=FALSE)## `summarise()` has grouped output by 'game', 'country'. You can override using
## the `.groups` argument.
## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.
3.1.2.3 Minecraft
plotFigure2a("Minecraft", sd=TRUE, debug=FALSE)## `summarise()` has grouped output by 'game', 'country'. You can override using
## the `.groups` argument.
## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.
3.1.2.4 Elden Ring
plotFigure2a("Elden Ring", sd=TRUE, debug=FALSE)## `summarise()` has grouped output by 'game', 'country'. You can override using
## the `.groups` argument.
## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.
3.1.2.5 FIFA22
plotFigure2a("FIFA22", sd=TRUE, debug=FALSE)## `summarise()` has grouped output by 'game', 'country'. You can override using
## the `.groups` argument.
## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.
3.1.2.6 Call of Duty: Vanguard
plotFigure2a("Call of Duty: Vanguard", sd=TRUE, debug=FALSE)## `summarise()` has grouped output by 'game', 'country'. You can override using
## the `.groups` argument.
## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.
3.2 Within each region, how much “internal” agreement is there on labels of interest?
In this case, we group countries to higher-level regions based on the World Value Survey map. We perform the same analysis but on a regional level.
# wrapper function that enable individual game
plotFigure2aRegion <- function(game = "All", sd=FALSE, debug=FALSE) {
var.df <- df %>% group_by(game, region, label) %>%
summarise(mean_answer = mean(answer, na.rm=TRUE),
variance = var(answer, na.rm=TRUE),
sd=sd(answer, na.rm=TRUE))
if(game == "All" && !sd) {
# average the variances across games for each region
region.df <- var.df %>% group_by(region, label) %>%
summarise(y=mean(variance, na.rm=TRUE))
} else if (game == "All" && sd) {
region.df <- var.df %>% group_by(region, label) %>%
summarise(y=mean(sd, na.rm=TRUE))
} else if(game != "All" && !sd) {
region.df <- var.df %>% filter(game == {{ game }}) %>% mutate(y=variance)
} else if (game != "All" && sd) {
region.df <- var.df %>% filter(game == {{ game }}) %>% mutate(y=sd)
} else {
print("Error...")
}
p1 <- plotVariance(region.df, gameTitle=game, sd=sd, binary=FALSE)
p2 <- plotVariance(region.df, gameTitle=game, sd=sd, binary=TRUE)
res <- ggarrange(p1, p2, ncol=1, heights = c(1, 6))
return(res)
}
# plot variance of a label across countries
plotVariance <- function(inputDf, gameTitle="All", sd=FALSE, binary=FALSE) {
# color the US
inputDf$color <- inputDf$region == "US"
plotDf <- inputDf[with(inputDf, order(label, region, y)),] %>%
filter(!label %in% c("control_complexity", "learning_curve", "difficulty", "replayability"))
if(!binary) {
plotDf <- inputDf[with(inputDf, order(label, region, y)),] %>%
filter(label %in% c("control_complexity", "learning_curve", "difficulty", "replayability"))
}
if(sd) {
yLabel <- "Standard Devaition"
} else {
yLabel <- "Variance"
}
p <- plotDf %>% ggplot(aes(x=reorder_within(region, y, label), y=y, color=color)) +
geom_point() +
scale_x_reordered() +
facet_wrap(~label, ncol=4, scales = "free") +
ylim(c(0, 3)) +
scale_color_manual(values=c("#999999", "#56B4E9")) +
labs(y = yLabel, x = NULL,
title = paste0("How much internal agreement is there on labels of interest (", gameTitle, ")"))
if(binary) {
p <- p + ylim(c(0, 1))
}
return(p)
}
4 Figure 2b: How do different populations view these labels in specific contexts?
To answer this question, we calculate differences in estimated mean label outcomes for each demographic using causal matching analysis. To predict the probability of having a game label in a certain country, we use multilevel regression and post-stratification (MRP).
# Import libraries
source("read.R")
library(ggalt)
library(reshape2)
options(dplyr.summarise.inform = FALSE)4.1 Calculate differences in estimated mean label outcomes for each demographic using causal matching analysis
We first check the differences between means for each labels across countries below. We run the following analyzeCountry function to each label and explores whether there is a significant difference between US and non-US participants in our matched dataframe.
analyzeCountry <- function(inputDf, label, lower=1, upper=3) {
df <- inputDf %>% filter(label == {{ label }}) %>%
mutate(is_us = ifelse(country == "US", 1, 0)) %>%
mutate(gender.no.nonbinary = ifelse(gender == "Male", 1, 0)) %>% # depending on how we want to deal with this
mutate(gender.no.nonbinary = factor(gender.no.nonbinary)) %>%
na.omit()
# causal matching using matchit
m.out <- matchit(is_us ~ game + ageCut + hardcore + gender.no.nonbinary, method = "nearest", distance = "mahalanobis", link = "probit", replace = TRUE, data=df)
# optional for debugging
s.out <- summary(m.out, standardize = TRUE)
plot(s.out)
matched.df <- match.data(m.out)
matched.df$is_us <- factor(matched.df$is_us)
model <- lm(answer ~ is_us, data=matched.df)
print(summary(model))
annotator <- df %>% filter(labeler == "Yes" & label == {{ label }}) %>%
count(game, answer) %>%
group_by(game) %>%
summarize(majority.vote = mean(answer[which(n==max(n))])) %>% ungroup()
p <- plotByGame(matched.df, "is_us", label, LOWER=lower, UPPER = upper) +
geom_point(aes(x=game, y=majority.vote, color="Polish Annotator", shape="Polish Annotator"), size=2, data=annotator) + # majority.vote is the Polish annotator majority vote
labs(title=paste0(label, " Score by Country"), y="Mean Score", x="Games") +
scale_color_discrete("Country")
return(p)
}- In the remainder of this section, we look at each individual label. For each label, we can look at whether there is a significant difference between US and non-US participants. For example, learning curve, replaybility, zen, space, violent, action, comedic, grinding, anime, motivation:action, motivation:social, motivation:immersion are significantly different.
4.1.1 Control Complexity
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.6644 -0.6257 0.3356 0.3743 1.3743
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.66443 0.05624 29.597 <2e-16 ***
## is_us1 -0.03875 0.05673 -0.683 0.495
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6865 on 8669 degrees of freedom
## Multiple R-squared: 5.384e-05, Adjusted R-squared: -6.151e-05
## F-statistic: 0.4667 on 1 and 8669 DF, p-value: 0.4945
4.1.2 Learning Curve
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7651 -0.6227 -0.6227 0.3773 1.3773
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.76510 0.06333 27.871 <2e-16 ***
## is_us1 -0.14236 0.06388 -2.228 0.0259 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7731 on 8669 degrees of freedom
## Multiple R-squared: 0.0005725, Adjusted R-squared: 0.0004572
## F-statistic: 4.966 on 1 and 8669 DF, p-value: 0.02588
4.1.3 Difficulty
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.90894 -0.90894 0.09106 0.10067 2.10067
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.899329 0.076427 24.852 <2e-16 ***
## is_us1 0.009613 0.077092 0.125 0.901
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9329 on 8669 degrees of freedom
## Multiple R-squared: 1.793e-06, Adjusted R-squared: -0.0001136
## F-statistic: 0.01555 on 1 and 8669 DF, p-value: 0.9008
4.1.4 Replayability
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4363 -0.4363 0.5637 0.5637 0.7047
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.29530 0.05902 38.892 <2e-16 ***
## is_us1 0.14098 0.05953 2.368 0.0179 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7204 on 8669 degrees of freedom
## Multiple R-squared: 0.0006465, Adjusted R-squared: 0.0005313
## F-statistic: 5.608 on 1 and 8669 DF, p-value: 0.0179
4.1.5 Pacifist
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.2013 -0.1641 -0.1641 -0.1641 0.8359
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20134 0.03039 6.626 3.65e-11 ***
## is_us1 -0.03730 0.03065 -1.217 0.224
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3709 on 8669 degrees of freedom
## Multiple R-squared: 0.0001708, Adjusted R-squared: 5.544e-05
## F-statistic: 1.481 on 1 and 8669 DF, p-value: 0.2237
4.1.6 Made for kids
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.3557 -0.3202 -0.3202 0.6798 0.6798
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.35570 0.03824 9.301 <2e-16 ***
## is_us1 -0.03547 0.03858 -0.920 0.358
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4668 on 8669 degrees of freedom
## Multiple R-squared: 9.754e-05, Adjusted R-squared: -1.781e-05
## F-statistic: 0.8456 on 1 and 8669 DF, p-value: 0.3578
4.1.7 Cozy
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.3044 -0.3044 -0.3044 0.6956 0.7248
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.27517 0.03768 7.302 3.08e-13 ***
## is_us1 0.02922 0.03801 0.769 0.442
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.46 on 8669 degrees of freedom
## Multiple R-squared: 6.817e-05, Adjusted R-squared: -4.718e-05
## F-statistic: 0.591 on 1 and 8669 DF, p-value: 0.4421
4.1.8 Zen
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.2773 -0.2773 -0.2773 0.7227 0.7920
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20805 0.03662 5.681 1.38e-08 ***
## is_us1 0.06923 0.03694 1.874 0.061 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.447 on 8669 degrees of freedom
## Multiple R-squared: 0.000405, Adjusted R-squared: 0.0002897
## F-statistic: 3.512 on 1 and 8669 DF, p-value: 0.06096
4.1.9 Fantasy
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.3340 -0.3340 -0.3340 0.6660 0.6711
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.32886 0.03864 8.511 <2e-16 ***
## is_us1 0.00510 0.03898 0.131 0.896
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4716 on 8669 degrees of freedom
## Multiple R-squared: 1.975e-06, Adjusted R-squared: -0.0001134
## F-statistic: 0.01712 on 1 and 8669 DF, p-value: 0.8959
4.1.10 Space
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.08054 -0.01877 -0.01877 -0.01877 0.98123
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.08054 0.01141 7.061 1.78e-12 ***
## is_us1 -0.06176 0.01150 -5.368 8.15e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1392 on 8669 degrees of freedom
## Multiple R-squared: 0.003313, Adjusted R-squared: 0.003198
## F-statistic: 28.82 on 1 and 8669 DF, p-value: 8.151e-08
4.1.11 Heroic
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.2537 -0.2537 -0.2537 0.7463 0.8054
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.19463 0.03560 5.467 4.69e-08 ***
## is_us1 0.05907 0.03591 1.645 0.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4345 on 8669 degrees of freedom
## Multiple R-squared: 0.000312, Adjusted R-squared: 0.0001967
## F-statistic: 2.706 on 1 and 8669 DF, p-value: 0.1
4.1.12 Real World
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.3624 -0.3545 -0.3545 0.6455 0.6455
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.362416 0.039197 9.246 <2e-16 ***
## is_us1 -0.007922 0.039538 -0.200 0.841
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4785 on 8669 degrees of freedom
## Multiple R-squared: 4.631e-06, Adjusted R-squared: -0.0001107
## F-statistic: 0.04014 on 1 and 8669 DF, p-value: 0.8412
4.1.13 Violent
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4066 -0.4066 -0.4066 0.5934 0.8054
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.19463 0.04012 4.851 1.25e-06 ***
## is_us1 0.21196 0.04047 5.237 1.67e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4898 on 8669 degrees of freedom
## Multiple R-squared: 0.003154, Adjusted R-squared: 0.003039
## F-statistic: 27.43 on 1 and 8669 DF, p-value: 1.669e-07
4.1.14 Action
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4703 -0.4703 -0.4703 0.5297 0.6778
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.32215 0.04085 7.886 3.5e-15 ***
## is_us1 0.14816 0.04121 3.596 0.000325 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4986 on 8669 degrees of freedom
## Multiple R-squared: 0.001489, Adjusted R-squared: 0.001374
## F-statistic: 12.93 on 1 and 8669 DF, p-value: 0.0003253
4.1.15 Emotional
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.2081 -0.1907 -0.1907 -0.1907 0.8093
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20805 0.03221 6.460 1.1e-10 ***
## is_us1 -0.01737 0.03249 -0.535 0.593
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3931 on 8669 degrees of freedom
## Multiple R-squared: 3.298e-05, Adjusted R-squared: -8.237e-05
## F-statistic: 0.2859 on 1 and 8669 DF, p-value: 0.5929
4.1.16 Comedic
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4488 -0.4488 -0.4488 0.5512 0.6309
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.36913 0.04073 9.063 <2e-16 ***
## is_us1 0.07971 0.04109 1.940 0.0524 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4972 on 8669 degrees of freedom
## Multiple R-squared: 0.000434, Adjusted R-squared: 0.0003187
## F-statistic: 3.764 on 1 and 8669 DF, p-value: 0.0524
4.1.17 Experimental
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.1745 -0.1632 -0.1632 -0.1632 0.8368
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.17450 0.03029 5.760 8.69e-09 ***
## is_us1 -0.01127 0.03056 -0.369 0.712
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3698 on 8669 degrees of freedom
## Multiple R-squared: 1.57e-05, Adjusted R-squared: -9.966e-05
## F-statistic: 0.1361 on 1 and 8669 DF, p-value: 0.7122
4.1.18 Strategy
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4488 -0.4488 -0.4488 0.5512 0.5571
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.442953 0.040750 10.870 <2e-16 ***
## is_us1 0.005885 0.041105 0.143 0.886
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4974 on 8669 degrees of freedom
## Multiple R-squared: 2.365e-06, Adjusted R-squared: -0.000113
## F-statistic: 0.0205 on 1 and 8669 DF, p-value: 0.8862
4.1.19 Grinding
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5116 -0.5116 0.4884 0.4884 0.6510
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.34899 0.04092 8.528 < 2e-16 ***
## is_us1 0.16262 0.04128 3.940 8.23e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4995 on 8669 degrees of freedom
## Multiple R-squared: 0.001787, Adjusted R-squared: 0.001672
## F-statistic: 15.52 on 1 and 8669 DF, p-value: 8.227e-05
4.1.20 Anime
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.17450 -0.07897 -0.07897 -0.07897 0.92103
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.17450 0.02228 7.831 5.40e-15 ***
## is_us1 -0.09552 0.02248 -4.250 2.16e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.272 on 8669 degrees of freedom
## Multiple R-squared: 0.002079, Adjusted R-squared: 0.001964
## F-statistic: 18.06 on 1 and 8669 DF, p-value: 2.16e-05
4.1.21 Hand drawn
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.14094 -0.09939 -0.09939 -0.09939 0.90061
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.14094 0.02459 5.732 1.02e-08 ***
## is_us1 -0.04155 0.02480 -1.675 0.0939 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3001 on 8669 degrees of freedom
## Multiple R-squared: 0.0003237, Adjusted R-squared: 0.0002083
## F-statistic: 2.807 on 1 and 8669 DF, p-value: 0.09391
4.1.22 Stylized
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5394 -0.5394 0.4606 0.4606 0.4832
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.51678 0.04084 12.65 <2e-16 ***
## is_us1 0.02265 0.04120 0.55 0.582
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4985 on 8669 degrees of freedom
## Multiple R-squared: 3.487e-05, Adjusted R-squared: -8.048e-05
## F-statistic: 0.3023 on 1 and 8669 DF, p-value: 0.5825
4.1.23 Motivation: Action
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5419 -0.5419 0.4581 0.4581 0.6067
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.39333 0.04067 9.671 < 2e-16 ***
## is_us1 0.14860 0.04103 3.622 0.000294 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4981 on 8649 degrees of freedom
## Multiple R-squared: 0.001514, Adjusted R-squared: 0.001399
## F-statistic: 13.12 on 1 and 8649 DF, p-value: 0.0002941
4.1.24 Motivation: Social
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4985 -0.4985 -0.3467 0.5015 0.6533
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.34667 0.04080 8.498 < 2e-16 ***
## is_us1 0.15186 0.04115 3.690 0.000226 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4996 on 8649 degrees of freedom
## Multiple R-squared: 0.001572, Adjusted R-squared: 0.001456
## F-statistic: 13.62 on 1 and 8649 DF, p-value: 0.0002256
4.1.25 Motivation: Mastery
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4607 -0.4607 -0.4607 0.5393 0.5800
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.42000 0.04070 10.32 <2e-16 ***
## is_us1 0.04065 0.04105 0.99 0.322
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4984 on 8649 degrees of freedom
## Multiple R-squared: 0.0001134, Adjusted R-squared: -2.252e-06
## F-statistic: 0.9805 on 1 and 8649 DF, p-value: 0.3221
4.1.26 Motivation: Achievement
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5292 -0.5292 0.4708 0.4708 0.5000
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.50000 0.04076 12.267 <2e-16 ***
## is_us1 0.02923 0.04112 0.711 0.477
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4992 on 8649 degrees of freedom
## Multiple R-squared: 5.843e-05, Adjusted R-squared: -5.718e-05
## F-statistic: 0.5054 on 1 and 8649 DF, p-value: 0.4772
4.1.27 Motivation: Immersion
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4948 -0.4948 -0.3733 0.5052 0.6267
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.37333 0.04080 9.149 < 2e-16 ***
## is_us1 0.12143 0.04116 2.950 0.00319 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4998 on 8649 degrees of freedom
## Multiple R-squared: 0.001005, Adjusted R-squared: 0.0008897
## F-statistic: 8.703 on 1 and 8649 DF, p-value: 0.003186
4.1.28 Motivation: Creativity
##
## Call:
## lm(formula = answer ~ is_us, data = matched.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4567 -0.4567 -0.4567 0.5433 0.5733
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.42667 0.04067 10.491 <2e-16 ***
## is_us1 0.02999 0.04103 0.731 0.465
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4981 on 8649 degrees of freedom
## Multiple R-squared: 6.175e-05, Adjusted R-squared: -5.386e-05
## F-statistic: 0.5341 on 1 and 8649 DF, p-value: 0.4649
4.2 To predict the probability of having a game label in a certain country, we use multilevel regression and post-stratification (MRP).
# Import libraries
source("read.R")
library(ggalt)
library(reshape2)
options(dplyr.summarise.inform = FALSE)In this subsection, we try to predict a probability of labeling a certain game with a particular Genome label by different countries.
# read csv
countryLevelDf <- read_csv("demographic.csv")## Rows: 7167 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Country, AgeGroup, Gender
## dbl (1): UserCount
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.countryLevelDf$UserCount <- as.numeric(countryLevelDf$UserCount)
# make the country name consistent with the individual survey dataframe
countryLevelDf <- countryLevelDf %>%
filter(Country %in% c("Argentina", "Brazil", "Chile", "Colombia", "Germany", "Greece", "India", "Japan", "Korea", "Mexico", "Nigeria", "Poland", "Saudi Arabia", "Singapore", "South Africa", "United States")) %>%
dplyr::rename(ageCut = AgeGroup, country = Country, gender = Gender) %>%
mutate(ageCut = case_when(ageCut == "18 - 24" ~ "18-24",
ageCut == "25 - 34" ~ "25-34",
ageCut == "35 - 44" ~ "35-44",
ageCut == "45 - 55" ~ "45-55",
ageCut == "> 55" ~ "55+"))
countryLevelDf$country[countryLevelDf$country == "United States"] <- "US"
countryLevelDf$country[countryLevelDf$country == "Saudi Arabia"] <- "Saudi.Arabia"
countryLevelDf$country[countryLevelDf$country == "South Africa"] <- "South.Africa"
countryLevelDf <- countryLevelDf %>% mutate(region = case_when(country %in% c("Japan", "Korea") ~ "Confucian",
country %in% c("Singapore", "India") ~ "West.South.Asia",
country %in% c("Argentina", "Chile", "Colombia", "Mexico", "Brazil") ~ "Latin.America",
country %in% c("Germany") ~ "Protestant.Europe",
country %in% c("South.Africa", "Nigeria", "Saudi.Arabia") ~ "Islamic",
country %in% c("Poland") ~ "Catholic.Europe",
country %in% c("Greece") ~ "Orthodox.Europe",
country %in% c("US") ~ "US"))
countryLevelDf$country <- factor(countryLevelDf$country)
countryLevelDf$ageCut <- factor(countryLevelDf$ageCut, c("18-24","25-34", "35-44", "45-55", "55+"))
countryLevelDf$gender <- factor(countryLevelDf$gender)
countryLevelDf$region = factor(countryLevelDf$region)
# Calculate the percentage of age+gender group by country
countryLevelDf <- countryLevelDf %>% group_by(country) %>%
mutate(UserCount.per = UserCount/sum(UserCount, na.rm=TRUE))
countryLevelDf <- countryLevelDf %>% filter(ageCut != "" & ageCut != "Unknown" & !is.na(ageCut)) %>%
filter(gender != "" & gender != "Unknown" & !is.na(gender)) %>%
mutate(gender = ifelse(gender == "Male", "Male", "Female or Nonbinary"))4.3 Exploratory Data Analysis
We examine the difference between our sample of ~5000 participants and the true MS population.
source("read.R")
df <- df %>% mutate(gender = ifelse(gender == "Male", "Male", "Female or Nonbinary"))
age_sample <- df %>%
dplyr::mutate(age = factor(ageCut, ordered = FALSE)) %>%
dplyr::group_by(age) %>%
dplyr::summarise(n = n()) %>%
dplyr::mutate(Sample = n/sum(n))
age_post <- countryLevelDf %>%
dplyr::mutate(age = factor(ageCut, ordered = FALSE)) %>%
dplyr::group_by(age) %>%
dplyr::summarise(n_post = sum(UserCount)) %>%
dplyr::mutate(Population = n_post/sum(n_post))
age <- dplyr::inner_join(age_sample, age_post, by = "age") %>% select(age, Sample, Population)
age_plot <- ggplot() +
ylab("") + xlab("Proportion") + theme_bw() + coord_flip() +
geom_dumbbell(data = age, aes(y = age, x = Sample, xend = Population)) +
geom_point(data = melt(age, id = "age"), aes(y = age, x = value, color = variable), size = 2) +
scale_x_continuous(limits = c(0, 0.5), breaks = c(0, .1, .2, .3, .4, .5)) +
ggtitle("Age")
age_plot
# Gender because the poststratification dataset does not have nonbinary
male_sample <- df %>%
dplyr::mutate(gender = ifelse(gender != "Male", "Female or Nonbinary", "Male")) %>%
dplyr::group_by(gender) %>%
dplyr::summarise(n = n()) %>%
dplyr::mutate(Sample = n/sum(n))
male_post <- countryLevelDf %>%
dplyr::group_by(gender) %>%
dplyr::summarise(n_post = sum(UserCount)) %>%
dplyr::mutate(Population = n_post/sum(n_post))
male <- dplyr::inner_join(male_sample, male_post, by = "gender") %>% select(gender, Sample, Population)
male_plot <- ggplot() +
ylab("") + xlab("") + theme_bw() + coord_flip() +
geom_dumbbell(data = male, aes(y = gender, x = Sample, xend = Population)) +
geom_point(data = melt(male, id = "gender"), aes(y = gender, x = value, color = variable), size = 2) +
scale_x_continuous(limits = c(0, 1), breaks = c(0, .2, .4, .6, .8, 1.0)) + ggtitle("Gender")
male_plot
4.3.1 Poststratification for country
- Here we further show that for the US, it is way higher likely that these games such as PUBG, Elden Ring, GTA, Call of Duty to be violent.
4.3.1.1 TODO: Add the hardcore percentage before merging the table
library(data.table)
poststratify <- function(game, label) {
temp <- df %>% filter(label == {{ label }} & game == {{ game }}) %>%
filter(accessibility %in% c("Yes", "No")) %>%
mutate(gender = ifelse(gender == "Male", "Male", "Female or Nonbinary")) %>%
na.omit()
mod <- glm(answer ~ hardcore + accessibility + country + gender + ageCut, data = temp, family = binomial(link="logit"))
post <- expand.grid(accessibility=unique(temp$accessibility),
country=unique(temp$country),
gender=unique(temp$gender),
ageCut=unique(temp$ageCut),
hardcore=unique(temp$hardcore))
post <- post %>%
mutate(gender = ifelse(gender != "Male", "Female", "Male")) %>%
left_join(countryLevelDf, by=c("country", "gender", "ageCut")) %>%
mutate(gender = ifelse(gender != "Male", "Female or Nonbinary", "Male"))
post <- post %>%
mutate(UserCount.per = ifelse(accessibility == "No", UserCount.per * 0.1, UserCount.per * 0.9)) # heuristic about accessibility
post$prediction <- predict(mod, newdata=post, type="response", allow.new.levels=TRUE)
post$weight.pred <- post$prediction*post$UserCount.per
results <- data.table(post)[ , .(final.est = sum(weight.pred, na.rm=TRUE)), by = .(country)]
return(results)
}
labelLst = c("pacifist", "made.for.kids", "cozy", "zen", "fantasy", "space", "heroic", "real.world", "violent",
"action", "emotional", "comedic", "experimental", "strategy", "grinding", "anime", "hand.drawn",
"stylized", "action.motivation", "social", "mastery", "achievement", "immersion", "creativity")
gameLst <- unique(df$game)
predicted.df <- data.frame(country=c(), final.est=c(), label=c(), game = c())
# Calculate the poststratified probability
for(g in gameLst) {
for(l in labelLst) {
label.df <- poststratify(g, l)
label.df$label <- l
label.df$game <- g
predicted.df <- rbind(predicted.df, label.df)
}
}## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred# View the countries with a probability > 0.75
predicted.df %>% filter(final.est > 0.5 & label == "violent")## country final.est label game
## 1: US 0.7451460 violent PUBG
## 2: Greece 0.5973012 violent PUBG
## 3: Japan 0.5699150 violent PUBG
## 4: Mexico 0.5149857 violent PUBG
## 5: Poland 0.5240003 violent PUBG
## 6: US 0.8926281 violent Grand Theft Auto V
## 7: Singapore 0.6421735 violent Grand Theft Auto V
## 8: South.Africa 0.5146014 violent Grand Theft Auto V
## 9: Brazil 0.5838548 violent Grand Theft Auto V
## 10: Chile 0.5220274 violent Grand Theft Auto V
## 11: Colombia 0.5268495 violent Grand Theft Auto V
## 12: Germany 0.7017908 violent Grand Theft Auto V
## 13: Greece 0.7147602 violent Grand Theft Auto V
## 14: Japan 0.5895735 violent Grand Theft Auto V
## 15: Korea 0.6300997 violent Grand Theft Auto V
## 16: Mexico 0.6231587 violent Grand Theft Auto V
## 17: Poland 0.5453907 violent Grand Theft Auto V
## 18: US 0.8339568 violent Elden Ring
## 19: Singapore 0.5073604 violent Elden Ring
## 20: Germany 0.5004755 violent Elden Ring
## 21: Greece 0.5778392 violent Elden Ring
## 22: Poland 0.5420260 violent Elden Ring
## 23: US 0.6905598 violent Call of Duty: Vanguard
## 24: Germany 0.5635139 violent Call of Duty: Vanguard
## 25: Greece 0.5231360 violent Call of Duty: Vanguard
## country final.est label game4.3.2 Alternative approach using rstanarm
https://bookdown.org/jl5522/MRP-case-studies/introduction-to-mrp.html
# res <- df %>% filter(label == "made.for.kids") %>%
# filter(!label %in% c("NA.positive.opinion", "NA.negative.opinion", "NA.feeling", "NA.art")) %>%
# filter(!is.na(country) & !is.na(gender) & !is.na(Language) & !is.na(ageCut)) %>%
# mutate(male = ifelse(gender == "Male", 1, 0)) %>%
# mutate(male = as.factor(male)) %>%
# select(Response.ID, country, male, ageCut, region, answer)
# library(rstanarm)
# fit <- stan_glmer(answer ~ (1|country) + (1|ageCut) + male + region,
# family = binomial(link = "logit"),
# data = res,
# prior = normal(0, 1, autoscale = TRUE),
# prior_covariance = decov(scale = 0.50),
# adapt_delta = 0.99,
# refresh = 0,
# seed = 1010)5 Figure 3: If label outcome differences are found across countries, is there a data-backed explanation relating to culture?
To answer this question, we use the Hofstede’s theory of cultural dimensions. This question answers whether culturally “closer” countries have more similar survey results.
- Define ‘culture’ using Hofstede’s cultural dimension map and calculate cultural similarity scores between pairs of countries
- Calculate correlations between sampled survey responses for individuals across pairs of countries
library(tidyverse)
library(tidytext)
library(data.table)
source("read.R")
theme_set(
theme_bw() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size=8))
)
labels <- unique(df$label)# Calculate correlations between sampled survey responses for individuals across pairs of countries
rnormt <- function(n, range, mu, s) {
# range is a vector of two values
F.a <- pnorm(min(range), mean = mu, sd = s)
F.b <- pnorm(max(range), mean = mu, sd = s)
u <- runif(n, min = F.a, max = F.b)
return(qnorm(u, mean = mu, sd = s))
}
countryCorr <- df %>% select(Response.ID, game, country, label, answer)
# Responses that are lower than 5 participants per game
summary <- countryCorr %>% filter(label == "learning_curve") %>%
group_by(country, game) %>%
summarise(n=n(), mean=mean(answer), sd=sd(answer)) %>%
ungroup() %>%
filter(n < 5)
# games that we will ignore altogether
gameTooFew <- unique(summary$game)
gameTooFew## [1] "The Sims 3" "Animal Crossing: New Horizons"
## [3] "Mario Kart 8" "Stardew Valley"
## [5] "Candy Crush"# Compare "label space" vector
updateVector <- function(inputCountry, inputrnormtA, inputLabel, lower=0, upper=1) {
# update the vector of a country based on a specific label
# Args:
# country: string of countries
# label: string of the label of interest
# lower, upper: the lower and upper bound (0-1 for binary, 0-3 for control_complexity etc, 0-4 for difficulty)
# Returns:
# an updated vector in the label space
inputLabel
inputCountry
if(upper - lower == 0) {
print("Something happened")
}
dfA <- df %>% filter(label == {{ inputLabel }}) %>%
mutate(answer = (1-0) * (answer-lower) / (upper-lower) + 0) %>% # the filter and mutate_at order is important and cannot be switched
filter(country == {{ inputCountry }}) %>%
filter(!game %in% gameTooFew) %>%
group_by(game) %>%
dplyr::summarise(mean=mean(answer, na.rm=TRUE), sd=sd(answer, na.rm=TRUE))
rnormtA <- inputrnormtA
for(row in 1:nrow(dfA)) {
meanA <- as.double(dfA[row, "mean"])
sdA <- as.double(dfA[row, "sd"])
if(sdA == 0) {
gameA <- rep(meanA, 1000)
} else {
gameA <- rnormt(1000, c(0, 1), meanA, sdA)
}
rnormtA <- c(rnormtA, gameA)
}
return(rnormtA)
}
labelSpace <- data.frame(matrix(ncol = 0, nrow = 28*600))
countries <- unique(df$country)
labels <- c("control_complexity", "learning_curve", "difficulty", "replayability", "pacifist", "made.for.kids", "cozy", "zen", "fantasy", "space", "heroic", "real.world", "violent",
"action", "emotional", "comedic", "experimental", "strategy", "grinding", "anime", "hand.drawn",
"stylized", "action.motivation", "social", "mastery", "achievement", "immersion", "creativity")
for(country in countries) {
v <- c()
for(label in labels) {
if(label %in% c("control_complexity", "learning_curve", "replayability")) {
v <- updateVector(country, v, label, lower=1, upper=3)
} else if (label == "difficulty") {
v <- updateVector(country, v, label, lower=1, upper=4)
} else {
v <- updateVector(country, v, label, lower=0, upper=1)
}
}
labelSpace <- cbind(labelSpace, as.data.frame(v))
names(labelSpace)[names(labelSpace) == "v"] <- country
}
library(corrplot)
mx <- cor(labelSpace, use = "complete.obs")
corrplot(mx, type = "upper", order = "FPC", tl.cex=2,
tl.col = "black", tl.srt = 45)
# Plot label correlation against the Hofstede scores
mx[upper.tri(mx, diag = TRUE)] <- NA
rownames(mx) <- colnames(mx)
mx <- na.omit(reshape::melt(t(mx)))
mx <- mx[ order(mx$X1, mx$X2), ]
labelCorr <- mx %>% select(X1, X2, value)
labelCorr <- labelCorr %>% dplyr::rename(countryA=X1, countryB=X2, label.corr=value)
labelCorr$countryA <- as.character(labelCorr$countryA)
labelCorr$countryB <- as.character(labelCorr$countryB)
labelCorr <- transform(labelCorr, countryA = pmin(countryA, countryB), countryB=pmax(countryA, countryB))
head(labelCorr)## countryA countryB label.corr
## 17 India US 0.3050486
## 33 Singapore US 0.3081656
## 49 South.Africa US 0.3761590
## 65 Nigeria US 0.2540979
## 81 Argentina US 0.3483207
## 97 Brazil US 0.3691277# 1. Find the pairwise correlation between countries based on the Hofstede's framework
# correlation based on six metrics: power.distance, individualism, masculinity, uncertainty
# long.term.orientation, and indulgence
library(reshape)
hofstedeDf <- read.csv("hoftstede.csv")
hofstedeDf <- hofstedeDf %>% select(-X) %>%
mutate(power.distance = power.distance / 100,
individualism = individualism / 100,
masculinity = masculinity / 100,
uncertainty = uncertainty / 100,
long.term.orientation = long.term.orientation / 100,
indulgence = indulgence / 100) # scale the hofstede score to 0 - 1
corrMatrix <- cor(t(hofstedeDf[, -1])) # remove the first column with countries
corrMatrix[upper.tri(corrMatrix, diag = TRUE)] <- NA
rownames(corrMatrix) <- colnames(corrMatrix) <- hofstedeDf$country
corrMatrix <- na.omit(reshape::melt(t(corrMatrix)))
corrMatrix <- corrMatrix[ order(corrMatrix$X1, corrMatrix$X2), ]
hofstedeCorr <- corrMatrix %>% select(X1, X2, value) %>% dplyr::rename(countryA=X1, countryB=X2, hofstede.corr=value)
hofstedeCorr$countryA <- as.character(hofstedeCorr$countryA)
hofstedeCorr$countryB <- as.character(hofstedeCorr$countryB)
hofstedeCorr <- transform(hofstedeCorr, countryA = pmin(countryA, countryB), countryB=pmax(countryA, countryB))
head(hofstedeCorr)## countryA countryB hofstede.corr
## 17 India Singapore 0.44105595
## 33 India US -0.40050181
## 49 India Nigeria -0.06628162
## 65 India South.Africa -0.48927712
## 81 Argentina India -0.35943139
## 97 Brazil India 0.18844511- This finding shows that there is no clear trend in the correlation between Hofstede’s dimensions of culture and the label correlations. It seems that the label correlation is roughly the same across these countries. In other words, the Hofstede’s dimensions of culture, which was observed in organizational culture at IBM, doesn’t seem to explain the difference well.
# Now plotting the two correlations
combined <- hofstedeCorr %>% left_join(labelCorr, by=c("countryA", "countryB"))
ggplot(aes(x=hofstede.corr, y=label.corr), data=combined) +
geom_point() +
ylim(c(0,1)) +
geom_smooth(method='lm')## `geom_smooth()` using formula 'y ~ x'
The following takes a similar approach, but looks at the individual game level.
# # Construct the matrix: 6000 * 16 (1000 simulation * 6 games) * 16 countries
# compareCountry <- function(countryA, countryB, label, lower=0, upper=1) {
# # compare the label scoring between two countries
# # Args:
# # countryA, countryB: string of countries
# # label: string of the label of interest
# # lower, upper: the lower and upper bound (0-1 for binary, 0-3 for control_complexity etc, 0-4 for difficulty)
# # Returns:
# # a dataframe that contains country A and country B, pearson correlation estimate, p.value, as well as the label
# dfA <- df %>% filter(label == {{ label }}) %>%
# mutate(answer = (1-0) * (answer-lower) / (upper-lower) + 0) %>% # the filter and mutate_at order is important and cannot be switched
# filter(country == {{ countryA }}) %>%
# filter(!game %in% gameTooFew) %>%
# group_by(game) %>%
# dplyr::summarise(mean=mean(answer, na.rm=TRUE), sd=sd(answer, na.rm=TRUE))
# dfB <- df %>% filter(label == {{ label }}) %>%
# mutate(answer = (1-0) * (answer-lower) / (upper-lower) + 0) %>%
# filter(country == {{ countryB }}) %>%
# filter(!game %in% gameTooFew) %>%
# group_by(game) %>%
# dplyr::summarise(mean=mean(answer, na.rm=TRUE), sd=sd(answer, na.rm=TRUE))
#
# rnormtA <- c()
# rnormtB <- c()
# for(row in 1:nrow(dfA)) {
# meanA <- as.double(dfA[row, "mean"])
# meanB <- as.double(dfB[row, "mean"])
# sdA <- as.double(dfA[row, "sd"])
# sdB <- as.double(dfB[row, "sd"])
#
# gameA <- rnormt(1000, c(lower, upper), meanA, sdA)
# gameB <- rnormt(1000, c(lower, upper), meanB, sdB)
# if(any(is.na(gameA)) | any(is.na(gameB))) next;
# rnormtA <- c(rnormtA, gameA)
# rnormtB <- c(rnormtB, gameB)
# }
#
# cor.res = cor.test(rnormtA, rnormtB, method=c("pearson"), use = "complete.obs")
#
# return(data.frame(countryA = countryA, countryB = countryB, estimate=cor.res$estimate, p=cor.res$p.value, label=label))
# }
#
# matrix <- combn(unique(df$country), 2) # get the combination of countries
# new.df <- data.frame(countryA=c(), countryB=c(), estimate=c(), p=c(), label=c())
#
# for(col in 1:ncol(matrix)) {
# # for each combination (English vs. language X
# cA <- matrix[1, col]
# cB <- matrix[2, col]
#
# for(label in c("control_complexity", "learning_curve", "replayability")) {
# comparison <- compareCountry(cA, cB, label, lower=1, upper=3)
# new.df <- rbind(new.df, comparison)
# }
# comparison <- compareCountry(cA, cB, "difficulty", lower=1, upper=4)
# new.df <- rbind(new.df, comparison)
#
# for(label in labels[!labels %in% c("control_complexity", "learning_curve", "difficulty", "replayability")]){
# comparison <- compareCountry(cA, cB, label, lower=0, upper=1)
# new.df <- rbind(new.df, comparison) # update the new.df
# }
# }
#
# # Here we look at labels with high positive or negative correlations
# new.df %>% filter(estimate > 0.1)6 Figure 4: How similar are label choices for bilingual speakers when asked questions in their native language versus English?
library(tidyverse)
library(tidytext)
library(data.table)
source("read.R")
theme_set(
theme_bw() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size=8))
)
df <- df %>% filter(!label %in% c("NA.positive.opinion", "NA.negative.opinion", "NA.feeling", "NA.art"))We ran our surveys in both English and non-English languages on bilingual speakers within each non-English speaking country.
rnormt <- function(n, range, mu, s) {
if(s == 0) {
return(c(NaN))
}
# range is a vector of two values
F.a <- pnorm(min(range), mean = mu, sd = s)
F.b <- pnorm(max(range), mean = mu, sd = s)
u <- runif(n, min = F.a, max = F.b)
return(qnorm(u, mean = mu, sd = s))
}# For some weird reason, I need to output the first four variable
compareLanguage <- function(languageA, languageB, inputCountries, label, lower=0, upper=1) {
languageA
languageB
inputCountries
label
dfA = df %>% dplyr::filter(label == {{ label }}) %>% # select the label
mutate(answer = (1-0) * (answer-lower) / (upper-lower) + 0) %>% # rescale the Likert scale answer to 0-1 (e.g., 1-3, 1-4 to 0-1)
dplyr::filter(Language == {{ languageA }} & country %in% inputCountries) %>%
group_by(game) %>%
summarise(mean=mean(answer, na.rm=TRUE), sd=sd(answer, na.rm=TRUE)) %>%
filter(!is.na(sd)) # in case there is only one response in a country
# same operation for languageB
dfB = df %>% dplyr::filter(label == {{ label }}) %>% # select the label
mutate(answer = (1-0) * (answer-lower) / (upper-lower) + 0) %>% # rescale the Likert scale answer to 0-1 (e.g., 1-3, 1-4 to 0-1)
dplyr::filter(Language == {{ languageB }} & country %in% inputCountries) %>%
group_by(game) %>%
summarise(mean=mean(answer, na.rm=TRUE), sd=sd(answer, na.rm=TRUE)) %>%
filter(!is.na(sd)) # in case there is only one response in a country
rnormtA <- c()
rnormtB <- c()
for(row in unique(df$game)) {
meanA <- as.double(dfA[dfA$game == row, "mean"])
meanB <- as.double(dfB[dfB$game == row, "mean"])
sdA <- as.double(dfA[dfA$game == row, "sd"])
sdB <- as.double(dfB[dfB$game == row, "sd"])
if(any(is.na(meanA)) | any(is.na(meanB))) next;
if(any(is.na(sdA)) | any(is.na(sdB))) next;
if(any(sdA == 0) | any(sdB == 0)) next;
gameA <- rnormt(1000, c(0, 1), meanA, sdA)
gameB <- rnormt(1000, c(0, 1), meanB, sdB)
rnormtA <- c(rnormtA, gameA)
rnormtB <- c(rnormtB, gameB)
}
# no game is available
if(length(rnormtA) == 0) {
return(data.frame(languageA = languageA, languageB = languageB, estimate=NaN, p=NaN, label=label))
}
cor.res = cor.test(rnormtA, rnormtB, method=c("pearson"), use = "complete.obs")
return(data.frame(languageA = languageA, languageB = languageB, estimate=cor.res$estimate, p=cor.res$p.value, label=label))
}matrix <- combn(unique(df$Language), 2) # get the combination of languages
new.df <- data.frame(languageA=c(), languageB=c(), estimate=c(), p=c(), label=c())
for(col in 1:ncol(matrix)) {
# for each combination (English vs. language X
l1 <- matrix[1, col] # English
if(l1 != "English") break
l2 <- matrix[2, col] # language X
language.countries <- df %>% filter(Language == l2) # find the correpondings countries to language X
for(label in c("control_complexity", "learning_curve", "replayability")) {
comparison <- compareLanguage(l1, l2, c(unique(language.countries$country)), label, lower=1, upper=3)
new.df <- rbind(new.df, comparison)
}
comparison <- compareLanguage(l1, l2, c(unique(language.countries$country)), "difficulty", lower=1, upper=4)
new.df <- rbind(new.df, comparison)
for(label in labels[!labels %in% c("control_complexity", "learning_curve", "difficulty", "replayability")]){
comparison <- compareLanguage(l1, l2, c(unique(language.countries$country)), label, lower=0, upper=1)
new.df <- rbind(new.df, comparison) # update the new.df
}
}- This result looks very cool as it indicates that the current translation of tags might be leaning toward popular markets. Japanese, Arabic, Greek markets are poorly translated.
new.df %>% filter(estimate < 0)## languageA languageB estimate p label
## cor88 English Greek -3.572007e-02 0.0504324223 pacifist
## cor90 English Greek -2.236068e-02 0.0613827682 cozy
## cor94 English Greek -2.098130e-03 0.8820876717 real.world
## cor107 English Greek -1.080524e-02 0.4026935824 mastery
## cor113 English Japanese -1.382350e-03 0.9303534221 replayability
## cor115 English Japanese -3.557793e-02 0.2610038057 pacifist
## cor119 English Japanese -3.656037e-02 0.2480574282 fantasy
## cor120 English Japanese -7.831736e-02 0.0004554858 heroic
## cor123 English Japanese -1.854424e-02 0.3099275849 action
## cor124 English Japanese -7.554202e-02 0.0168816502 emotional
## cor154 English Polish -2.320086e-02 0.0723358633 grinding
## cor155 English Polish -2.690210e-03 0.8829049323 hand.drawn
## cor158 English Polish -3.096397e-05 0.9980867074 social
## cor166 English Arabic -4.337230e-02 0.1705364952 difficulty
## cor167 English Arabic -1.732631e-02 0.5841988618 fantasy
## cor169 English Arabic -1.251937e-02 0.6925348295 violent
## cor173 English Arabic -4.157085e-02 0.1890113883 achievement